Creating a data set
# Load necessary library
library(tibble)
# Manually enter the data
data <- tibble(
Country = c("Japan", "South Korea", "Taiwan", "New Zealand", "China", "Bangladesh", "India", "Egypt", "Iraq", "Algeria", "Iran", "Libya", "Gabon", "Mauritius", "Togo", "Rwanda", "Ethiopia", "Liberia", "Venezuela", "Nicaragua", "United States", "UK", "Norway", "Northern Cyprus", "Montenegro", "France", "Switzerland", "Russia", "Ukraine", "Belarus", "Kyrgyzstan", "Uzbekistan", "Turkmenistan"),
Region = c("Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Middle East and North Africa", "Middle East and North Africa", "Middle East and North Africa", "Middle East and North Africa", "Middle East and North Africa", "Sub-Saharan Africa", "Sub-Saharan Africa", "Sub-Saharan Africa", "Sub-Saharan Africa", "Sub-Saharan Africa", "Sub-Saharan Africa", "Americas", "Americas", "Americas", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Former Soviet Union", "Former Soviet Union", "Former Soviet Union", "Former Soviet Union", "Former Soviet Union", "Former Soviet Union"),
PercentageBelieveVaccinesSafe = c(30, 40, 50, 60, 70, 80, 90, 100, 30, 40, 50, 60, 70, 80, 90, 100, 30, 40, 50, 60, 70, 80, 90, 100, 30, 40, 50, 60, 70, 80, 90, 100, 30)
)
# You would continue to fill in the data for each country and their respective percentage
# Write the data to a CSV file
write.csv(data, "vaccine_belief_data.csv", row.names = FALSE)
# Print out the data to verify
print(data)## # A tibble: 33 × 3
## Country Region PercentageBelieveVaccinesSafe
## <chr> <chr> <dbl>
## 1 Japan Asia 30
## 2 South Korea Asia 40
## 3 Taiwan Asia 50
## 4 New Zealand Asia 60
## 5 China Asia 70
## 6 Bangladesh Asia 80
## 7 India Asia 90
## 8 Egypt Middle East and North Africa 100
## 9 Iraq Middle East and North Africa 30
## 10 Algeria Middle East and North Africa 40
## # ℹ 23 more rowsfirst redesign code
library(plotly)## Loading required package: ggplot2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readr)
vaccine_data <- read_csv("vaccine_belief_data.csv")## Rows: 33 Columns: 3## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Country, Region
## dbl (1): PercentageBelieveVaccinesSafe
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Define the unique regions, including an 'All' option
unique_regions <- unique(vaccine_data$Region)
all_regions <- c('All', unique_regions)
# Create a list for dropdown menu options
dropdown_buttons <- lapply(all_regions, function(region) {
if (region == 'All') {
return(list(method = "restyle",
args = list('visible', rep(TRUE, length(unique_regions))),
label = region))
} else {
return(list(method = "restyle",
args = list('visible',
lapply(unique_regions, function(x) x == region)),
label = region))
}
})
# Plotly does not natively support this kind of interaction in a straightforward way.
# So we prepare the data by creating a separate trace for each region.
plot_data <- lapply(unique_regions, function(region) {
subset(vaccine_data, Region == region)
})
# Create the initial plot
p <- plot_ly()
# Add traces to the plot for each region
for(i in seq_along(plot_data)) {
p <- add_trace(p, data = plot_data[[i]], x = ~Country, y = ~PercentageBelieveVaccinesSafe,
type = 'bar', name = unique_regions[i], visible = TRUE)
}
# Add the dropdown menu to the plot
final_plot <- p %>% layout(title = "Percentage of People Who Believe Vaccines Are Safe by Country and Global Region",
xaxis = list(title = "Country"),
yaxis = list(title = "Percentage Who Believe Vaccines Are Safe"),
updatemenus = list(
list(
active = -1,
buttons = dropdown_buttons
)
))
# Show the plot
final_plotSecond redesign code
library(shiny)
library(plotly)
library(readr)
library(dplyr)
# Define the UI
ui <- fluidPage(
titlePanel("Vaccine Safety Belief by Country and Region"),
sidebarLayout(
sidebarPanel(
numericInput("percentage", "Percentage threshold:", min = 0, max = 100, value = 50),
textInput("country", "Country:", ""),
actionButton("update", "Update View")
),
mainPanel(plotlyOutput("plot"))
)
)
# Define the server logic
server <- function(input, output) {
# Reactive value for the filtered dataset
filtered_data <- reactive({
req(input$update) # This makes sure the code waits for the update button to be pressed
data <- read_csv("vaccine_belief_data.csv")
data <- data %>%
filter(PercentageBelieveVaccinesSafe <= input$percentage,
grepl(input$country, Country, ignore.case = TRUE))
})
# Render the Plotly plot
output$plot <- renderPlotly({
fig <- plot_ly(filtered_data(), x = ~PercentageBelieveVaccinesSafe, y = ~Country, type = 'scatter', mode = 'markers+text',
text = ~paste(Country, ':', PercentageBelieveVaccinesSafe, '%'), textposition = 'right',
marker = list(size = 10), color = ~Region, hoverinfo = 'text') %>%
layout(title = 'Vaccine Safety Belief by Country and Region',
xaxis = list(title = 'Percentage Believing Vaccines Are Safe'),
yaxis = list(title = ''))
fig
})
# Update the view when the button is pressed
observeEvent(input$update, {
filtered_data()
})
}
# Run the application
shinyApp(ui = ui, server = server)